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climate change

The Catawba Riverkeepers provide their take on the water and environment issues facing the Catawba River watershed and surrounding areas.

[youtube=http://www.youtube.com/watch?v=ut130iDhaKo]

Since I’m posting this on January 1st, I suppose I should offer some prognostications for Charlotte area water resources in the coming year. I’m not much at predictions, but I can offer up some of the things that I know will happen over the next year.

We will hear from the Supreme Court about whether Duke Energy and others will be allowed to join in the South Carolina versus North Carolina suit over water allocations in the Catawba River watershed.

Sprawl will continue to impact streams around the region, though development will be a bit slower than at its peak.

Water conservation will, unfortunately, continue to be at the back of most people’s minds, unless there happens to be a major flood or drought in the news. Lawns will be watered even on rainy days and with upward-directed droplets at noon. People will wash cars on their driveways and run their dishwashers after every meal. They may even leave the water running while they brush their teeth.

It is not that there was no October literature to pick. My time to read articles simply disappeared in the lead-up to and excitement of the Geological Society of America meeting. This month, however, I am back on track and I will try to update this post as I move through the last few weeks of November.

Fussel, H-M. 2009. An updated assessment of the risks from climate change based on research published since the IPCC Fourth Assessment Report. Climatic Change (2009) 97:469–482. doi:10.1007/s10584-009-9648-5
The takeaway message is this: While some topics are still under debate (e.g., changes to tropical cyclones), most recent research indicates that things are looking even worse now than we thought a few years ago. Greenhouse gas emissions are rising faster than we anticipated, and we have already committed to substantial warming, which is currently somewhat masked by high aerosol concentrations. It is increasingly urgent to find mitigation and adaptation strategies. Not good.

Gardner, LR. 2009. Assessing the effect of climate change on mean annual runoff. Journal of Hydrology. 379 (3-4): 351-359. doi:10.1016/j.jhydrol.2009.10.021
This fascinating article starts by showing a strong correlation (r2 = 0.94) between mean annual runoff and a function of potential evapotranspiration and precipitation. The author then goes on to derive an equation that shows how temperature increases can be used to calculate the change in evapotranspiration, therefore solving the water budget and allowing the calculation of the change in mean annual runoff. Conversely, the same equation can be used to solve for the necessary increase in precipitation to sustain current runoff under different warming scenarios.

Schuler, T. V., and U. H. Fischer. 2009.Modeling the diurnal variation of tracer transit velocity through a subglacial channel, J. Geophys. Res., 114, F04017, doi:10.1029/2008JF001238.
The authors made multiple dye tracer injections into a glacial moulin and then measured discharge and tracer breakthrough at the proglacial channel. They found strong hysteresis in the relationship between tracer velocity and proglacial discharge and attributed this hysteresis to the adjustment of the size of a subglacial Röthlisberger channel to hydraulic conditions that change over the course of the day. Cool!

Bense, V. F., G. Ferguson, and H. Kooi (2009), Evolution of shallow groundwater flow systems in areas of degrading permafrost, Geophys. Res. Lett., 36, L22401, doi:10.1029/2009GL039225.
Warming temperatures in the Arctic and sub-arctic are lowering the permafrost table and activating shallow groundwater systems, causing increasing baseflow discharge of Arctic rivers. This paper shows how the groundwater flow conditions adjust to lowering permafrost over decades to centuries and suggests that even if air temperatures are stabilized, baseflow discharge will continue to increase for a long time.

Soulsby, Tetzlaff, and Hrachowitz. Tracers and transit times: Windows for viewing catchment scale storage. Hydrological Processes. 23(24): 3503 – 3507. doi: 10.1002/hyp.7501
In this installment of Hydrological Processes series of excellent invited commentaries, Soulsby and colleagues remind readers that although flux measurements have been the major focus of hydrologic science for decades, it is storage that is most relevant for applied water resources problems. They show that tracer-derived estimates of mean transit time combined with streamflow measurements can be used to calculate the amount of water stored in the watershed. They use their long-term study watersheds in the Scottish Highlands to illustrate how transit time and storage scale together and correlate with climate, physiography, and soils in the watersheds. Finally, they argue that while such tracer-derived storage estimates have uncertainties and are not a panacea, they do show promise across a range of scales and geographies.

Chatanantavet, P., and G. Parker (2009), Physically based modeling of bedrock incision by abrasion, plucking, and macroabrasion, J. Geophys. Res., 114, F04018, doi:10.1029/2008JF001044.
Over the past 2 decades, geomorphologists have developed much better insight into the landscape evolution of mountainous areas by developing computerized landscape evolution models. A key component of such models is the stream power rule for bedrock incision, but some have complained that is not physically based enough to describe. In this paper, the authors lay out a new model for bedrock incision based on the mechanisms of abrasion, plucking, and macroabrasion (fracturing and removal of rock by the impact of moving sediment) and incorporating the hydrology and hydraulics of mountain rivers. This could be an influential paper.
Payn, R. A., M. N. Gooseff, B. L. McGlynn, K. E. Bencala, and S. M. Wondzell (2009), Channel water balance and exchange with subsurface flow along a mountain headwater stream in Montana, United States, Water Resour. Res., 45, W11427, doi:10.1029/2008WR007644.
Tracer tests were conducted along 13 continuous reaches of a mountain stream to quantify gross change in discharge versus net loss and net gain. Interestingly, the change in discharge over some reaches did not correspond to calculations of net loss or net gain based on tracer recovery. These results suggests that commonly used methods for estimating exchange with subsurface flow may not be representing all fluxes. Bidirectional exchange with the subsurface, like that found in this paper, is likely to be very important for nutrient processing and benthic ecology.

Please note that I can’t read the full article of AGU publications (including WRR, JGR, and GRL) until July 2010 or the print issue arrives in my institution’s library. Summaries of those articles are based on the abstract only.

Some of these papers will be useful for my teaching (Fraley et al. and Schenk and Hupp), one will be useful in revising a paper from my Ph.D. research (Navarre-Sitchler et al.), and the rest are of general research for on-going projects or projects in the design stage. I hope they give you a flavor of the sort of things that set spinning the research gears in my mind.

Last week, the Southeastern United States received several inches of snow. This late season snowfall was certainly a novelty, though not an unprecedented occurrence. But it did stir up conversations among local residents, especially when the week ended with ~25 degree Celsius (75 Fahrenheit) sunshine. The weather’s fickleness also got me thinking about climate variability and climate change and how easily we can slip up and confuse the two. I even see scientists (who should know better) conflating variability and change, so below I offer a short, illustrated tutorial on the differences.

Hydrometeorological variables are things like precipitation, streamflow, groundwater levels, temperature, and humidity and are often expressed as annual or seasonal averages. The average value of one of those variables over 30 years is called a climatological normal. Below, I’ve illustrated a hypothetical climate variable as it varies of a 30 year period. These normals are redefined every 10 years, so right now we are using 1971-2000 as our normal period.

Figure 1. A hypothetical climate variable through time

The average value of the variable is 0.5, and the squiggles above and below the mean represent climate variability. I’ll define climate variability as the oscillations around a mean state. (An aside: it’s fairly common to see a few years in a row that are below the mean or above the mean, in a phenomenon known as serial correlation, where the value of a variable is influenced by the values that precede it. As an example, if you have a severe drought one year, even if it rains more than normal the next year, streamflow may stay quite low as groundwater is replenished. This is what is happening in the southeast now after our 2007 drought.)

Variability then is all about the oscillations, but it doesn’t tell you anything about what’s happening with the mean. Below, I’ve illustrated the same time series shifted progressively by 0.003 per time step. Here the mean is changing, while the variability stays the same.

Figure 2. A hypothetical climate variable in blue is trending by 0.003 per year (with the non-trending time series in gray for comparison)

As in the illustration above, variables like average temperature and sea surface temperature are experiencing changes in their mean values. So, climate change can take the form of a trend in the mean value of a variable over time. A climatological variable experiencing change in the mean would not have the same “normal” values from one climate normal period to the next.

But climate change can also affect the variability of a variable, as illustrated below. Here the mean is not changing, but I’ve made below-mean points successively lower by 0.0067 per time step and above mean points are successively higher by 0.00347 per time step.

Figure 3. A hypothetical climate variable (blue) showing an increase in variability with time (gray line is the variable with unchanging mean and variability)

This sort of change is the sort of change we might see in precipitation in some areas. For example, the Southeastern United States is predicted to have more intense summer rainfall and more intense droughts, and retrospective trend studies suggest that this may already be the case. Even though the mean precipitation is not changing, the Southeastern United States is still experiencing a climate change effect manifested in a change in climate variability.

Finally, climate change can take the form of a trend in the mean and a trend in the variability, as shown below.

Figure 4. A hypothetical climate variable with changing mean and variability (gray solid line indicates variable with unchanging mean and variability, gray dotted line has a changing mean without changing variability)

This final pattern may be the case for streamflow in some regions. Mean streamflow could decrease because of increasing evapotranspirative losses in a warmer climate, and streamflow variability could increase because of changes in precipitation and drought intensity. This sort of complicated pattern may occur for other climatological variables as well.

So what does this mean for “freak” late winter snowstorms in the southeastern United States? Climate change trending towards warmer temperatures makes frozen precipitation less likely (Figure 2), but given the variability inherent in meteorological systems (Figure 1), I wouldn’t rule it out entirely. But the snowshoes in my garage are still feeling a bit neglected.

Much existing research has focused on detecting climate change effects on snowmelt-dominated watersheds, but in the Pacific Coast and Cascades ranges, precipitation falls as either rain or snow, depending on latitude, elevation, and season. Watersheds often straddle the snow line, with some areas dominantly receiving rain and higher elevations accumulating seasonal snowpacks. These snowpacks are near the 0°C threshold, making them sensitive to the effects of climate warming. Climate sensitivity of seasonal and event hydrographs from watersheds with mixed rain and snow has not been fully explored. This project investigates detectable climate change signals in long-term streamflow records in the Washington, Oregon, and northern California Coast and Cascades Ranges.

Watersheds with mean elevations above the seasonal snow line show significant increases in streamflow during January through March and decreases in the percent of annual flow during April through June, the historical snowmelt period. These changes were not detectable in watersheds with mean elevations below the seasonal snow line. There were no consistent trends in peakflow dates or volumes. The multiple drivers of peakflow occurrence make it unlikely that any changes in peakflow timing will be detectable for several decades. Results suggest that Coast Range hydrology has been minimally impacted by historical climate warming, but that Cascades Range watersheds are already experiencing altered hydrologic regimes.

Pending acceptance, the work will be presented in session H32 Spatial and Temporal Trends in Hydrometeorological Records as Indicators of Climate Variability and Change.